Recommendation of adaptive learning pathways for college Chinese based on reinforcement learning

Abstract

Deep reinforcement learning offers a robust solution to the challenge of adaptive learning path optimization in higher education, where student heterogeneity and curriculum complexity often undermine traditional instructional models. This paper introduces a technically advanced recommendation framework for university-level Chinese language courses, which models student knowledge states and behavioral data as a Markov Decision Process and applies a deep Q-network to predict optimal content sequencing. The system monitors progress, time efficiency and participation thru a multidimensional reward mechanism to achieve teaching goals. The experiment collected a large amount of interaction data between students and other students. The proposed method is compared with the existing rule-based system. The findings show that the accuracy and efficiency of the customized curriculum are greatly improved. The accuracy of learning tasks increased by 20%, and the average completion time decreased by 30%. The modular architecture allows for scalable deployment, efficient data processing, and precise feature engineering in heterogeneous educational environments. Limitations are principally related to the dependency on high-quality annotated data and the computational demands of model training, although the system exhibits significant robustness and generalizability within the tested context. These results show that deep reinforcement learning is useful in intelligent education systems. It provides a reference point for future technological development and encourages adaptive teaching.